Google brings 45 teraflops tensor flow processors to its compute cloud

Enlarge / The 180 TFLOPS Cloud TPU card. (credit: Google)

Google has developed its second-generation tensor processor-four 45-teraflops chips packed onto a 180 TFLOPS tensor processor unit (TPU) module, to be used for machine learning and artificial intelligence-and the company is bringing it to the cloud. TPU-based computation will be available to Google Cloud Compute later this year.

Typically in machine-learning workloads, initial training and model building are divided from the subsequent pattern matching against the model. The former workload is the one that is most heavily dependent on massive compute power, and it's this that has generally been done on GPUs. Google's first-generation TPUs were used for the second part-making inferences based on the model, to recognize images, language, or whatever. Those first generation custom chips are 15 to 30 times faster and 30 to 80 times more power-efficient than CPUs and GPUs for these workloads, and the company has been using them already for its AlphaGo Go-playing computer, as well as its search results.

The new TPUs are optimized for both workloads, allowing the same chips to be used for both training and making inferences. Each card has its own high-speed interconnects, and 64 of the cards can be linked into what Google calls a pod, with 11.5 petaflops total; one petaflops is 10¹⁵ floating point operations per second.

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